{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "login success!\n",
      "logout success!\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1405.16x488.75 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "黄金分割0.382:9.76\n",
      "黄金分割0.618:10.4\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1405.16x488.75 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1405.16x488.75 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [jump_power, changeRatio, Close, Volume]\n",
      "Index: []\n",
      "Empty DataFrame\n",
      "Columns: [jump_power, changeRatio, Close, Volume]\n",
      "Index: []\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1405.16x488.75 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import baostock as bs\n",
    "import pandas as pd\n",
    "import mplfinance as mpf\n",
    "import numpy as np\n",
    "\n",
    "class DefTypesPool():\n",
    "    def __init__(self):\n",
    "        self.routes = {}\n",
    "    # 注册绘图函数\n",
    "    def route_types(self, types_str):\n",
    "        def decorator(f):\n",
    "            self.routes[types_str] = f\n",
    "            return f\n",
    "        return decorator\n",
    "    # 发现绘图函数\n",
    "    def route_output(self, path):\n",
    "        function_val = self.routes.get(path)\n",
    "        if function_val:\n",
    "            return function_val\n",
    "        else:\n",
    "            raise ValueError('Route \"{}\"\" has not been registered'.format(path))\n",
    "        \n",
    "class MplTypesDraw():\n",
    "    mpl = DefTypesPool() \n",
    "                          \n",
    "\n",
    "class MplVisualIf(MplTypesDraw):\n",
    "    def __init__(self):\n",
    "        MplTypesDraw.__init__(self)\n",
    "    \n",
    "\n",
    "\n",
    "    \n",
    "    def fig_output2(self, **kwargs):\n",
    "        self.index =kwargs['index']\n",
    "        df_dat = kwargs['draw_kind']['ochl']\n",
    "       \n",
    "        pdata = pd.DataFrame.from_dict(df_dat)\n",
    " \n",
    "        apds = []\n",
    "        mpf_hlines = {}\n",
    "       \n",
    "        hlines_number = []\n",
    "        hlines_color = []\n",
    "        if kwargs['draw_kind'].__contains__('annotate'):\n",
    "            annotate = kwargs['draw_kind']['annotate']\n",
    "            maxdf = annotate['max']['andata']\n",
    "            mindf = annotate['min']['andata']\n",
    "            pdata['max'] = np.nan\n",
    "            pdata['min'] = np.nan\n",
    "            # 组织scatter\n",
    "            for index,row in maxdf.iterrows():\n",
    "                pdata.loc[index,'max'] = pdata.loc[index,'Close']\n",
    "            for index,row in mindf.iterrows():\n",
    "                pdata.loc[index,'min'] = pdata.loc[index,'Close']\n",
    "            if len(maxdf) > 0 and len(mindf) > 0:\n",
    "                apds = [mpf.make_addplot(pdata['max'],type='scatter',markersize=100,marker='^',color='r'),mpf.make_addplot(pdata['min'],type='scatter',markersize=100,marker='v',color='g')]\n",
    "            elif len(maxdf) > 0:\n",
    "                apds = [mpf.make_addplot(pdata['max'],type='scatter',markersize=100,marker='^',color='r')]\n",
    "            elif len(mindf) > 0:\n",
    "                apds = [mpf.make_addplot(pdata['min'],type='scatter',markersize=100,marker='v',color='g')]\n",
    "      \n",
    "\n",
    "        if kwargs['draw_kind'].__contains__('hline'):\n",
    "            hline = kwargs['draw_kind']['hline']\n",
    "            for index,item in hline.items():\n",
    "                 hlines_number.append(item['pos'])\n",
    "                 hlines_color.append(item['c'])\n",
    "        mpf_hlines=dict(hlines=hlines_number,colors=hlines_color,linestyle='--',linewidths=3)\n",
    "        mc = mpf.make_marketcolors(up='red',down='green',edge='inherit',wick='black',volume='in')\n",
    "        s = mpf.make_mpf_style(marketcolors=mc, gridstyle='--',rc={'font.family': 'SimHei'},mavcolors=['orange','blue','purple','yellow'])\n",
    "        mpf.plot( data = pdata, addplot=apds, type='ohlc',title = kwargs['title'] ,style = s, ylabel = u'价格', xlabel = u'日期',figratio=(23,8),figscale=0.85,hlines=mpf_hlines)\n",
    "       \n",
    "\n",
    "\n",
    "# 登陆系统\n",
    "def bs_k_data_stock(code_val='sz.000651',start_val = '2025-01-01', end_val='2025-05-01',freq_val='d',adjust_val='3'):\n",
    "    lg = bs.login()\n",
    "    # 获取历史行情数据\n",
    "    fields = \"date,open,high,low,close,volume\"\n",
    "    df_bs = bs.query_history_k_data_plus(code_val, fields, start_date=start_val, end_date=end_val,frequency=freq_val ,adjustflag=adjust_val)\n",
    "    #日K线，3，默认不赋权，1 后复权，2 前复权\n",
    "    data_list= []\n",
    "    while (df_bs.error_code == '0') & df_bs.next():\n",
    "        #获取一条记录，将记录合并在一起\n",
    "        data_list.append(df_bs.get_row_data())\n",
    "    result = pd.DataFrame(data_list, columns=df_bs.fields)\n",
    "\n",
    "    result.close = result.close.astype('float64')\n",
    "    result.open = result.open.astype('float64')\n",
    "    result.low = result.low.astype('float64')\n",
    "    result.high = result.high.astype('float64')\n",
    "    result.volume = result.volume.astype('float64')\n",
    "    result.volume = result.volume/100 #单位转换:股-手\n",
    "    result.date = pd.DatetimeIndex(result.date)\n",
    "    result.set_index(\"date\", drop=True, inplace= True)\n",
    "    result.index = result.index.set_names('Date')\n",
    "\n",
    "    recon_data = {'High' : result.high, 'Low': result.low, 'Open': result.open, 'Close':result.close, 'Volume': result.volume}\n",
    "    df_recon = pd.DataFrame(recon_data)\n",
    "\n",
    "    #退出系统\n",
    "    bs.logout()\n",
    "    return df_recon\n",
    "\n",
    "app = MplVisualIf()\n",
    "\n",
    "# 周线\n",
    "def draw_kweek_chart(stock_dat):\n",
    "    # 周期重采样\n",
    "    # rule='W'周how=last()最后一天closed='right'右闭合label='right'右标签\n",
    "    Freq_T = 'W-FRI'\n",
    "\n",
    "    # 周线Close等于一周中最后一个交易日Close\n",
    "    week_dat = stock_dat.resample(Freq_T, closed='right',label='right').last()\n",
    "    # 周线Open等于一周中第一个交易日Close\n",
    "    week_dat.Open = stock_dat.Open.resample(Freq_T, closed='right', label='right').first()\n",
    "    # 周线High等于一周中High的最大值\n",
    "    week_dat.High = stock_dat.High.resample(Freq_T, closed='right', label='right').max()\n",
    "    # 周线Low等于一周中Low的最小值\n",
    "    week_dat.Low = stock_dat.Low.resample(Freq_T, closed='right',label='right').min()\n",
    "    # 周线Volume等于一周中Volume的总和\n",
    "    week_dat.Volume = stock_dat.Volume.resample(Freq_T, closed='right',label='right').sum()\n",
    "    # 第二种写法\n",
    "    weekly_df = stock_dat.resample(Freq_T).agg({'Open':'first','High':'max','Low':'min','Close':'last','Volume':'sum'})\n",
    "\n",
    "    layout_dict = {'figsize':(14,7),\n",
    "                   'index': weekly_df.index,\n",
    "                   'draw_kind': {\n",
    "                       'ochl':\n",
    "                       {\n",
    "                           'Open':weekly_df.Open,\n",
    "                           'Close':weekly_df.Close,\n",
    "                           'High':weekly_df.High,\n",
    "                           'Low':weekly_df.Low\n",
    "                       }\n",
    "                   },\n",
    "                   'title': u'周k线',\n",
    "                   'ylabel':u\"价格\"\n",
    "                   }\n",
    "    app.fig_output2(**layout_dict)\n",
    "\n",
    "def draw_fibonacci_chart(stock_dat):\n",
    "    # 黄金分割线\n",
    "    Fib_max = stock_dat.Close.max()\n",
    "    Fib_maxid = stock_dat.index.get_loc(stock_dat.Close.idxmax())\n",
    "    Fib_min = stock_dat.Close.min()\n",
    "    Fib_minid = stock_dat.index.get_loc(stock_dat.Close.idxmin())\n",
    "    Fib_382 = (Fib_max - Fib_min) * 0.382 + Fib_min\n",
    "    Fib_618 = (Fib_max - Fib_min) * 0.618 + Fib_min\n",
    "    print(u'黄金分割0.382:{}'.format(round(Fib_382, 2)))\n",
    "    print(u'黄金分割0.618:{}'.format(round(Fib_618, 2)))\n",
    "    max_df = stock_dat[stock_dat.Close == stock_dat.Close.max()]\n",
    "    min_df = stock_dat[stock_dat.Close == stock_dat.Close.min()]\n",
    "    \n",
    "    #绘制K线图+支撑/阻力\n",
    "    layout_dict = {'figsize':(14,7),\n",
    "                   'index':stock_dat.index,\n",
    "                   'draw_kind':{\n",
    "                       'ochl':{\n",
    "                           'Open':stock_dat.Open,\n",
    "                           'Close':stock_dat.Close,\n",
    "                           'High':stock_dat.High,\n",
    "                           'Low':stock_dat.Low\n",
    "                       },\n",
    "                       'hline':\n",
    "                       {\n",
    "                           'Fib_382':{\n",
    "                               'pos':Fib_382,\n",
    "                               'c': 'r'\n",
    "                           },\n",
    "                           'Fib_618':{\n",
    "                               'pos':Fib_618,\n",
    "                               'c':'g'\n",
    "                           }\n",
    "                       },\n",
    "                       'annotate':{\n",
    "                           u'max':{\n",
    "                               'andata':max_df ,\n",
    "                               'va':'top',\n",
    "                               'xy_y':'High',\n",
    "                               'xytext':(-30, stock_dat.Close.mean()),\n",
    "                               'fontsize': 8,\n",
    "                               'arrow':dict(facecolor='red', shrink = 0.1)\n",
    "                           },\n",
    "                           u'min':{\n",
    "                               'andata':min_df,\n",
    "                               'va':'bottom',\n",
    "                               'xy_y':'Low',\n",
    "                               'xytext':(-30, -stock_dat.Close.mean()),\n",
    "                               'fontsize': 8,\n",
    "                               'arrow':dict(facecolor='green', shrink = 0.1)\n",
    "                           }\n",
    "                       }\n",
    "                   },\n",
    "                   'title':'支撑/阻力位',\n",
    "                   'ylabel':u'价格',\n",
    "                   'legend':u'best'}\n",
    "    app.fig_output2(**layout_dict)\n",
    "\n",
    "def draw_gap_annotate(stock_dat):\n",
    "    # 绘制K线图\n",
    "    # 挖掘跳空缺口\n",
    "    jump_threshold = stock_dat.Close.median() * 0.01 # 跳空阈值 收盘价中位数*0.01\n",
    "    stock_dat['changeRatio'] = stock_dat.Close.pct_change() * 100 # 计算涨/跌幅（今收-作收)/昨收*100% 判断向上跳空缺口/向下跳空缺口\n",
    "    stock_dat['preLow'] = stock_dat.Low.shift(1) # 增加昨天最低价序列\n",
    "    stock_dat['preHigh'] = stock_dat.High.shift(1) # 增加昨日最高价序列\n",
    "    stock_dat = stock_dat.assign(jump_power = 0)\n",
    " \n",
    "   \n",
    "    stock_dat['jump_power'] = stock_dat.apply(lambda row:apply_gap(row['changeRatio'],row['preLow'],row['preHigh'],row['Low'],row['High'],jump_threshold),axis = 1)\n",
    "    up_jump = stock_dat[(stock_dat.changeRatio > 0) & (stock_dat.jump_power > 0)]\n",
    "    down_jump = stock_dat[(stock_dat.changeRatio < 0) & (stock_dat.jump_power) < 0]\n",
    "\n",
    "    layout_dict = {\n",
    "        'figsize': (14, 7),\n",
    "        'index': stock_dat.index,\n",
    "        'draw_kind': {\n",
    "            'ochl': {\n",
    "                'Open': stock_dat.Open,\n",
    "                'Close': stock_dat.Close,\n",
    "                'High': stock_dat.High,\n",
    "                'Low': stock_dat.Low\n",
    "            },\n",
    "            'annotate':\n",
    "            {\n",
    "                u'max':\n",
    "                {\n",
    "                    'andata': up_jump,\n",
    "                    'va':'top',\n",
    "                    'xy_y':'preHigh',\n",
    "                    'xytext': (0,-stock_dat['Close'].mean() * 0.5),\n",
    "                    'fontsize': 8,\n",
    "                    'arrow': dict(facecolor='red', shrink=0.1)\n",
    "                },\n",
    "                u'min':\n",
    "                {\n",
    "                    'andata':down_jump,\n",
    "                    'va': 'bottom',\n",
    "                    'xy_y':'preLow',\n",
    "                    'xytext':(0, stock_dat['Close'].mean() * 0.5),\n",
    "                    'fontsize': 8,\n",
    "                    'arrow': dict(facecolor='green', shrink=0.1)\n",
    "                }\n",
    "            }\n",
    "        },\n",
    "        'title':u\"跳空缺口\",\n",
    "        'ylabel':u\"价格\"\n",
    "    }\n",
    "     # 显示向上跳空缺口\n",
    "    print(up_jump.filter(['jump_power','preClose','changeRatio','Close','Volume']))\n",
    "    # 显示向下跳空缺口\n",
    "    print(down_jump.filter(['jump_power','preClose','changeRatio','Close','Volume']))\n",
    "    app.fig_output2(**layout_dict)\n",
    "\n",
    "def apply_gap(changeRatio, preLow, preHigh, Low, High, threshold):\n",
    "    jump_power = 0\n",
    "    if (changeRatio > 0) and ((Low - preHigh) > threshold):\n",
    "        # 向上跳空（今最低-昨最高）/阈值\n",
    "        jump_power = (Low - preHigh) / threshold # 正数\n",
    "    elif (changeRatio < 0) and ((preLow - High) > threshold):\n",
    "        # 向下跳空（今最高-昨最低）/阈值\n",
    "        jump_power = (High - preLow) / threshold #负数\n",
    "    return jump_power\n",
    "\n",
    "# 唐奇安通道\n",
    "def get_ndays_signal(stock_dat, N1= 15, N2 = 5):\n",
    "    # 海龟策略，唐奇安通道突破（N日突破）买入/卖出信号\n",
    "    stock_dat['N1_High'] = stock_dat.High.rolling(window=N1).max() #计算最近N1个交易日最高价\n",
    "    expan_max = stock_dat.High.expanding().max() #滚动计算当前交易日为止的最大值\n",
    "    stock_dat.fillna({'N1_High':expan_max},inplace=True) #填充前N1个nan\n",
    "    stock_dat['N2_Low'] = stock_dat.Low.rolling(window=N2).min() #计算最近N2个交易日最低价\n",
    "    expan_min = stock_dat.Low.expanding().min()\n",
    "    stock_dat.fillna({'N2_Low':expan_min},inplace=True) #目前出现过的最小值填充前N2个nan\n",
    "    # 收盘价超过N1最高价，买入股票\n",
    "    buy_index = stock_dat[stock_dat.Close > stock_dat.N1_High.shift(1)].index\n",
    "    # 收盘价超过N2最高价，卖出股票\n",
    "    sell_index = stock_dat[stock_dat.Close < stock_dat.N2_Low.shift(1)].index\n",
    "    stock_dat.loc[buy_index, 'NSignal'] = 1\n",
    "    stock_dat.loc[sell_index,'NSignal'] = -1\n",
    "    stock_dat['NSignal'] = stock_dat.NSignal.shift(1)\n",
    "    stock_dat.ffill( inplace= True) #与前面元素保持一致\n",
    "    stock_dat.fillna({'NSignal':-1},inplace = True) #序列最前面几个NaN值用-1填充\n",
    "    return stock_dat\n",
    "\n",
    "def draw_ndays_annotate(stock_dat):\n",
    "    # 绘制唐奇安通道突破/N日突破\n",
    "    signal_shift = stock_dat.NSignal.shift(1)\n",
    "    signal_shift.fillna(value=-1, inplace=True) #序列最前面的NaN使用-1填充\n",
    "    list_signal = np.sign(stock_dat.NSignal - signal_shift) #计算买卖点\n",
    "\n",
    "    down_cross = stock_dat[list_signal < 0]\n",
    "    up_cross = stock_dat[list_signal > 0]\n",
    "\n",
    "    layout_dict = {'figsize':(14 ,7),\n",
    "                   'index':stock_dat.index,\n",
    "                   'draw_kind':{\n",
    "                       'ochl':{\n",
    "                           'Open':stock_dat.Open,\n",
    "                           'Close':stock_dat.Close,\n",
    "                           'High':stock_dat.High,\n",
    "                           'Low':stock_dat.Low\n",
    "                       },\n",
    "                       'line':{\n",
    "                           'N1_High':stock_dat.N1_High,\n",
    "                           'N2_Low':stock_dat.N2_Low\n",
    "                       },\n",
    "                       'annotate':{\n",
    "                           u'max':\n",
    "                           {\n",
    "                               'andata':down_cross,\n",
    "                               'va':'top',\n",
    "                               'xy_y':'N2_Low',\n",
    "                               'xytext':(-10, -stock_dat['Close'].mean()),\n",
    "                               'fontsize':8,\n",
    "                               'arrow':dict(facecolor='green', shrink=0.1)\n",
    "                           },\n",
    "                           u'min':\n",
    "                           {\n",
    "                               'andata':up_cross,\n",
    "                               'va':'bottom',\n",
    "                               'xy_y':'H1_High',\n",
    "                               'xytext':(-10, stock_dat['Close'].mean()),\n",
    "                               'fontsize': 8,\n",
    "                               'arrow': dict(facecolor='red',shrink=0.1)\n",
    "                           }\n",
    "                       }\n",
    "                   },\n",
    "                   'title':u'唐奇安通道突破',\n",
    "                   'ylabel':u\"价格\",\n",
    "                   'xlabel':u'日期',\n",
    "                   'xticks':15,\n",
    "                   'legend':u'best',\n",
    "                   'xticklabels':'%Y-%m-%d'}\n",
    "    app.fig_output2(**layout_dict)\n",
    "\n",
    "codelist = ['sh.601229']\n",
    "\n",
    "for code in codelist:\n",
    "    df_stockload = bs_k_data_stock(code,'2025-01-01', '2025-09-22')\n",
    "    draw_ndays_annotate(get_ndays_signal(df_stockload.copy(deep=True))) #海龟策略-唐奇安通道突破（N日突破）买入/卖出信号\n",
    "    draw_fibonacci_chart(df_stockload)\n",
    "    draw_kweek_chart(df_stockload)\n",
    "    draw_gap_annotate(df_stockload)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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